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Hierarchical One-Class Classifier With Within-Class Scatter-Based Autoencoders
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DOI：10.1109/TNNLS.2020.3015860.png)
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Autoencoding is a vital branch of representation learning in deep neural networks (DNNs). The extreme learning machine-based autoencoder (ELM-AE) has been recently developed and has gained popularity for its fast learning speed and ease of implementation. However, the ELM-AE uses random hidden node parameters without tuning, which may generate meaningless encoded features. In this brief, we first propose a within-class scatter information constraint-based AE (WSI-AE) that minimizes both the reconstruction error and the within-class scatter of the encoded features. We then build stacked WSI-AEs into a one-class classification (OCC) algorithm based on the hierarchical regularized least-squared method. The effectiveness of our approach was experimentally demonstrated in comparisons with several state-of-the-art AEs and OCC algorithms. The evaluations were performed on several benchmark data sets.
Keyword:
Tuning
Learning systems
Nonhomogeneous media
Neural networks
Minimization
Training
Feature extraction
Autoencoder (AE)
extreme learning machine (ELM)
one-class classification (OCC)
scatter matrix
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